Artificial intelligence based technologies for improving patient intake

Abstract

Artificial intelligence (AI) based technologies for improving patient intake are disclosed herein. An example method includes receiving a patient intake request from a user; initiating, based on the patient intake request, a patient intake data stream including verbal responses from the user regarding patient intake of the user; applying, while simultaneously receiving the patient intake data stream, a natural language processing (NLP) model to the verbal responses from the user to output (i) textual transcriptions and (ii) intent interpretations; generating, by a care plan generation module, a recommended care plan based on the textual transcriptions and the intent interpretations; identifying, by an intent interpretation fulfillment module, one or more recipient entities to receive the textual transcriptions and the recommended care plan; and transferring the textual transcriptions and the recommended care plan to one or more recipient entity devices of the one or more recipient entities.

What is claimed: 
     
         1 . An artificial intelligence (AI) based method for improving patient intake, the method comprising:
 receiving, at a server including a natural language processing (NLP) model and one or more processors, a patient intake request from a user, wherein the NLP model is trained with a plurality of verbal responses regarding patient intake of a plurality of users, and the NLP model is configured to output one or more textual transcriptions and one or more intent interpretations corresponding to the one or more textual transcriptions;   initiating, by the one or more processors and based on the patient intake request, a patient intake data stream including verbal responses from the user regarding patient intake of the user;   applying, by the one or more processors while simultaneously receiving the patient intake data stream, the NLP model to the verbal responses from the user to output (i) textual transcriptions corresponding to the verbal responses from the user and (ii) intent interpretations corresponding to the textual transcriptions;   generating, by a care plan generation module, a recommended care plan based on the textual transcriptions and the intent interpretations;   identifying, by an intent interpretation fulfillment module utilizing an application programming interface (API), one or more recipient entities to receive the textual transcriptions and the recommended care plan based on the intent interpretations; and   transferring, by the one or more processors, the textual transcriptions and the recommended care plan to one or more recipient entity devices of the one or more recipient entities to enable the one or more recipient entity to view the textual transcriptions and the recommended care plan by:
 mediating, by the one or more processors, quest tokens that enable access to a follow-up consult between the user and a recipient entity; and 
 upon initiation of the follow-up consult, verifying, by the one or more processors, meeting information of the follow-up consult between the user and the recipient entity. 
   
     
     
         2 . The AI based method of  claim 1 , wherein initiating the patient intake data stream further comprises:
 receiving, at the server, a phone call from the user; and   applying, by the one or more processors, the NLP model to the verbal responses in order to communicate with the user to complete the patient intake of the user.   
     
     
         3 . The AI based method of  claim 1 , wherein generating the recommended care plan further comprises:
 retrieving, by the care plan generation module, a set of medical diagnostic rules from one or more medical diagnostic libraries; and   applying, by the care plan generation module, the set of medical diagnostic rules to the textual transcriptions and the intent interpretations to automatically generate the recommended care plan.   
     
     
         4 . The AI based method of  claim 3 , wherein the one or more medical diagnostic libraries are stored in a memory of the server. 
     
     
         5 . The AI based method of  claim 1 , wherein transferring the textual transcriptions and the recommended care plan to the one or more recipient entity devices further comprises:
 receiving, from a recipient entity device, a follow-up consult request; and   scheduling, by a telehealth scheduling microservice, the user for a follow-up consult with the recipient entity through an automated virtual assistant that contacts the user via at least one of: (i) a short message service (SMS) text message or (ii) a phone call.   
     
     
         6 . The AI based method of  claim 1 , wherein transferring the textual transcriptions and the recommended care plan to the one or more recipient entity devices further comprises:
 receiving, at the one or more processors, an in-home monitoring request from a recipient entity;   provisioning, by the one or more processors, a cellular gateway, a monitoring device, and networking access for the cellular gateway and the monitoring device;   automatically scheduling, by the one or more processors, the cellular gateway and the monitoring device for shipment to the user;   receiving, at the one or more processors, data from the monitoring device corresponding to the user; and   causing, by the one or more processors, the data to render at a medical provider user interface in real-time.   
     
     
         7 . The AI based method of  claim 1 , wherein the one or more recipient entities includes a healthcare provider device to receive the textual transcriptions and the recommended care plan based on the intent interpretations. 
     
     
         8 . An artificial intelligence (AI) based system for improving patient intake, the system comprising:
 one or more processors; and   one or more memories communicatively coupled with the one or more processors, the one or more memories storing a care plan generation module, an intent interpretation fulfillment module, and a natural language processing (NLP) model that is trained with a plurality of verbal responses regarding patient intake of a plurality of users, and the NLP model is configured to output one or more textual transcriptions and one or more intent interpretations corresponding to the one or more textual transcriptions, and   wherein the one or more memories store instructions thereon that, when executed by the one or more processors, cause the one or more processors to:
 receive a patient intake request from a user, 
 initiate, based on the patient intake request, a patient intake data stream including verbal responses from the user regarding patient intake of the user, 
 apply, while simultaneously receiving the patient intake data stream, the NLP model to the verbal responses from the user to output (i) textual transcriptions corresponding to the verbal responses from the user and (ii) intent interpretations corresponding to the textual transcriptions, 
 generate, by the care plan generation module, a recommended care plan based on the textual transcriptions and the intent interpretations, 
 identify, by the intent interpretation fulfillment module utilizing an application programming interface (API), one or more recipient entities to receive the textual transcriptions and the recommended care plan based on the intent interpretations, and 
 transfer the textual transcriptions and the recommended care plan to one or more recipient entity devices of the one or more recipient entities to enable the one or more recipient entity to view the textual transcriptions and the recommended care plan by:
 mediating quest tokens that enable access to a follow-up consult between the user and a recipient entity; and 
 upon initiation of the follow-up consult, verifying meeting information of the follow-up consult between the user and the recipient entity. 
 
   
     
     
         9 . The AI based system of  claim 8 , wherein the instructions, when executed, further cause the one or more processors to initiate the patient intake data stream by:
 receiving a phone call from the user; and   applying the NLP model to the verbal responses in order to communicate with the user to complete the patient intake of the user.   
     
     
         10 . The AI based system of  claim 8 , wherein the instructions, when executed, further cause the one or more processors to generate the recommended care plan by:
 retrieving, by the care plan generation module, a set of medical diagnostic rules from one or more medical diagnostic libraries; and   applying, by the care plan generation module, the set of medical diagnostic rules to the textual transcriptions and the intent interpretations to automatically generate the recommended care plan.   
     
     
         11 . The AI based system of  claim 10 , wherein the one or more medical diagnostic libraries are stored in the one or more memories. 
     
     
         12 . The AI based system of  claim 8 , wherein the one or more memories further store a telehealth scheduling microservice, and the instructions, when executed, further cause the one or more processors to transfer the textual transcriptions and the recommended care plan to the one or more recipient entity devices by:
 receiving, from a recipient entity device, a follow-up consult request; and   scheduling, by the telehealth scheduling microservice, the user for a follow-up consult with the recipient entity through an automated virtual assistant that contacts the user via at least one of: (i) a short message service (SMS) text message or (ii) a phone call.   
     
     
         13 . The AI based system of  claim 8 , wherein the instructions, when executed, further cause the one or more processors to transfer the textual transcriptions and the recommended care plan to the one or more recipient entity devices by:
 receiving an in-home monitoring request from a recipient entity;   provisioning a cellular gateway, a monitoring device, and networking access for the cellular gateway and the monitoring device;   automatically scheduling the cellular gateway and the monitoring device for shipment to the user;   receiving data from the monitoring device corresponding to the user; and   causing the data to render at a medical provider user interface in real-time.   
     
     
         14 . The AI based system of  claim 8 , wherein the one or more recipient entities includes a healthcare provider device to receive the textual transcriptions and the recommended care plan based on the intent interpretations. 
     
     
         15 . A tangible, non-transitory computer-readable medium storing instructions for improving patient intake, that when executed by one or more processors cause the one or more processors to:
 receive a patient intake request from a user;   initiate, based on the patient intake request, a patient intake data stream including verbal responses from the user regarding patient intake of the user;   apply, while simultaneously receiving the patient intake data stream, a natural language processing (NLP) model to the verbal responses from the user to output (i) textual transcriptions corresponding to the verbal responses from the user and (ii) intent interpretations corresponding to the textual transcriptions, wherein the NLP model is trained with a plurality of verbal responses regarding patient intake of a plurality of users, and the NLP model is configured to output one or more textual transcriptions and one or more intent interpretations corresponding to the one or more textual transcriptions;   generate a recommended care plan based on the textual transcriptions and the intent interpretations;   identify, utilizing an application programming interface (API), one or more recipient entities to receive the textual transcriptions and the recommended care plan based on the intent interpretations; and   transfer the textual transcriptions and the recommended care plan to one or more recipient entity devices of the one or more recipient entities to enable the one or more recipient entity to view the textual transcriptions and the recommended care plan by:
 mediating guest tokens that enable access to a follow-up consult between the user and a recipient entity; and 
 upon initiation of the follow-up consult, verifying meeting information of the follow-up consult between the user and the recipient entity. 
   
     
     
         16 . The tangible, non-transitory computer-readable medium of  claim 15 , wherein the instructions, when executed, further cause the one or more processors to generate the recommended care plan by:
 retrieving a set of medical diagnostic rules from one or more medical diagnostic libraries; and   applying the set of medical diagnostic rules to the textual transcriptions and the intent interpretations to automatically generate the recommended care plan.   
     
     
         17 . The tangible, non-transitory computer-readable medium of  claim 15 , wherein the instructions, when executed, further cause the one or more processors to transfer the textual transcriptions and the recommended care plan to the one or more recipient entity devices by:
 receiving an in-home monitoring request from a recipient entity;   provisioning a cellular gateway, a monitoring device, and networking access for the cellular gateway and the monitoring device;   automatically scheduling the cellular gateway and the monitoring device for shipment to the user;   receiving data from the monitoring device corresponding to the user; and   causing the data to render at a medical provider user interface in real-time.